Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration

Ding Yang, Danilo Di Stefano, Michela Turrin, Sevil Sariyildiz, Yimin Sun*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
73 Downloads (Pure)


Simulation-Based Multi-Objective Optimization (SBMOO) methods are being increasingly used in conceptual architectural design. They mostly focus on the solving, rather than the re-formulation, of a Multi-Objective Optimization (MOO) problem. However, Optimization Problem Re-Formulation (Re-OPF) is necessary for treating ill-defined conceptual architectural design as an iterative exploration process. The paper proposes an innovative SBMOO method which builds in a dynamic and interactive Re-OPF phase. This Re-OPF phase, as the main novelty of the proposed method, aims at achieving a realistic MOO model (i.e., a parametric geometry-simulation model which includes important objectives, constraints, and design variables). The proposed method is applied to the conceptual design of a top-daylighting system, focusing on divergent concept generation. The integration of software tools Grasshopper and modeFRONTIER is adopted to support this application. The main finding from this application is that the proposed method can help to achieve quantitatively better and qualitatively more diverse Pareto solutions.

Original languageEnglish
Article number103251
Number of pages27
JournalAutomation in Construction
Publication statusPublished - 2020

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Conceptual architectural design
  • Divergent concept generation
  • GH-MF integration
  • Hierarchical clustering
  • Hierarchical variable structure
  • Information and knowledge extraction
  • Optimization problem re-formulation
  • Self-organizing map
  • Simulation-based multi-objective optimization
  • Top-daylighting system


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